ライフサイエンスコーパス: prediction method

1 age of these results to develop a structural prediction method.

2 ovement of our IntFOLD-TS tertiary structure prediction method.

3 ure was generated using the nearest template prediction method.

4 atorial counting approach independent of any prediction method.

5 ossible directions for further improving the prediction method.

6 ys produced by the Rosetta de novo structure prediction method.

7 not others, we tested a theoretical pattern prediction method.

8 ic domain computer programs, and used in our prediction method.

9 t been included in any current computational prediction method.

10 from hundreds to thousands, depending on the prediction method.

11 ixed model (G-BLUP) and a Bayesian (Bayes C) prediction method.

12 s and assessing the performance of footprint prediction methods.

13 tic evaluation of ten publicly available AMP prediction methods.

14 ng PconsC in comparison with earlier contact prediction methods.

15 ficant challenge for computational structure prediction methods.

16 and increase the power of protein structure prediction methods.

17 and compared it with the available competing prediction methods.

18 , underlies numerous potential functions and prediction methods.

19 rforms the four off-the-shelf subchloroplast prediction methods.

20 a simplified system for testing new affinity prediction methods.

21 gnificantly outperformed alternate, analogue prediction methods.

22 ying the framework to direct protein complex prediction methods.

23 ting phylogenies can be used as features for prediction methods.

24 s substitutes in the absence of good epitope prediction methods.

25 een used to evaluate many other binding site prediction methods.

26 RNA structure prediction by RNAG over extant prediction methods.

27 ble benchmark compound for crystal structure prediction methods.

28 e future models created by protein structure prediction methods.

29 ent of successful protein tertiary structure prediction methods.

30 bias in estimating the accuracy of function prediction methods.

31 large-scale evaluation of sequence-based SDP prediction methods.

32 ble with the accuracy of secondary structure prediction methods.

33 ld be generally avoided in protein structure prediction methods.

34 key step of template-based protein structure prediction methods.

35 nction and can be of great help for tertiary prediction methods.

36 the Critical Assessment of Protein Structure Prediction methods.

37 77.3%, which is the best among the existing prediction methods.

38 en protein sequence using a number of domain prediction methods.

39 ing support vector machines (SVMs) and other prediction methods.

40 er comparative modeling or de novo structure prediction methods.

41 ch as those produced by neural network-based prediction methods.

42 nformation to developers as well as users of prediction methods.

43 the accuracy of automated protein structure prediction methods.

44 ell as the state-of-the-art binding affinity prediction methods.

45 members, i.e. are too small for such contact prediction methods.

46 esian framework represent the main family of prediction methods.

47 urgent need for unbiased haploinsufficiency prediction methods.

48 ith significant improvement over existing MP prediction methods.

49 A target sites and improve miRNA target site prediction methods.

50 ent progress and challenges in RNA structure prediction methods.

51 sted, motivating our efforts to benchmark pI prediction methods.

52 compare against the current leading contact prediction methods.

53 g contact information into protein structure prediction methods.

54 ASP-winning template-based protein structure prediction methods.

55 framework that can be applied to driver gene prediction methods.

56 ve quality of binding predictions over other prediction methods.

57 presents new challenges to protein function prediction methods.

58 re dependency that should be considered by a prediction method?

59 re dependency that should be considered by a prediction method?

60 Using computational prediction methods, 20 of the remaining 62 variants were

61 ctions and outperform available whole genome prediction methods (74% versus 83% prediction accuracy).

62 A sequence-based prediction method able to accurately predict the propens

63 On a non-redundant test set, our epitope prediction method achieves 44% recall at 14% precision a

64 It outperforms other prediction methods, achieving an AUC of 0.92 compared to

65 organism communities with improved orthology prediction methods allowing pathway inference for 22 spe

66 lly obtain the results of the various domain prediction methods along with a consensus prediction.

67 unction makes correct assessment of function prediction methods an issue of great importance.

68 Here, we use a novel secondary structure prediction method and duplex-end differential calculatio

69 We utilized an unbiased sub-motif prediction method and reported CW as the representative

70 In recent years, successful contact prediction methods and contact-guided ab initio protein

71 ndings assess state-of-the-art cancer driver prediction methods and develop a new and improved consen

72 We have integrated several operon prediction methods and developed a consensus approach to

73 Both well-established hot spot prediction methods and new approaches to analyze individ

74 ting the knowledge encoded by different sRNA prediction methods and optimally aggregating them as pot

75 f-based modeling is complementary to current prediction methods and provides a promising direction in

76 sers to compare and choose between different prediction methods and provides estimates of the expecte

77 and highlights the need for generalized risk prediction methods and the inclusion of more diverse ind

78 were calculated using RosettaNMR, a de novo prediction method, and final structure calculations were

79 ing methods outperform benchmark branchpoint prediction methods, and can produce high-accuracy result

80 Risk factor epidemiology, prediction methods, and causal inference strategies are

81 Compared with state-of-the-art risk prediction methods, AnnoPred achieves consistently impro

82 All proteome predictions and the PROFtmb prediction method are available at http://www.rostlab.or

83 ated procedure for carrying out this epitope prediction method are presented.

84 Theoretical structure prediction methods are an attractive alternative.

85 Computational gene prediction methods are an important component of whole g

86 Existing cancer driver prediction methods are based on very different assumptio

87 Existing homology-based structure prediction methods are designed for globular, water-solu

88 Automated protein function prediction methods are needed to keep pace with high-thr

89 g increasingly commonplace, existing miR-TSV prediction methods are not designed to analyze these dat

90 se are summarized, and their calibration and prediction methods are overviewed.

91 ditional experimental approaches and current prediction methods are still unreliable.

92 Comp-utational function prediction methods are therefore essential as initial st

93 estigation, semiempirical NMR chemical shift prediction methods are used to evaluate the dynamically

94 d algorithms and that alchemical free energy predictions methods are close to becoming a mainstream t

95 red the accuracies of four genomic-selection prediction methods as affected by marker density, level

96 shown the comparative effectiveness of each prediction method, as well as provided guidelines as to

97 in interaction data sets, for development of prediction methods, as well as in the studies of the pro

98 viously described missense mutation function prediction methods at discriminating known oncogenic mut

99 con compared favorably with state-of-the-art prediction methods at the CASP6 meeting.

100 Our experimental results show that our IDR prediction method AUCpreD outperforms existing popular d

101 sent CONTRAfold, a novel secondary structure prediction method based on conditional log-linear models

102 A new prediction method based on consensus is described.

103 ximately 300 GPa using an unbiased structure prediction method based on evolutionary algorithm.

104 A reliable phenotype prediction method based on genetic sequence analysis cou

105 ng and Zhou develop a non-parametric genetic prediction method based on latent Dirichlet Process regr

106 ene patterns further, we propose an ortholog prediction method based on our gene pattern mining algor

107 A prediction method based on the physicochemical propertie

108 encing Project, we tested the utility of the prediction method based on the ratio of non-synonymous t

109 udies, structure refinement and for function prediction methods based on geometrical comparisons of l

110 roteins, we demonstrate that membrane domain prediction methods based on such a compact representatio

111 n capacities of the local backbone structure prediction methods based on the I-sites library by a sig

112 The prediction method behind DBD identifies sequence-specifi

113 odel refinements and alternate RNA structure prediction methods beyond the physics-based ones.

114 r small proteins using the Rosetta structure prediction method, but for larger and more complex prote

115 obustness, we also develop a committee-based prediction method by pooling together multiple personali

116 ecent CASP11 blind test of protein structure prediction methods by incorporating residue-residue co-e

117 ave developed an ab initio protein structure prediction method called chunk-TASSER that uses ab initi

118 Although contemporary structure prediction methods can assemble the correct topology for

119 Prediction methods can bridge this sequence-structure ga

120 swer comprehensively, while state-of-the-art prediction methods can.

121 continues to be a difficult task with a few prediction methods clearly taking the lead; none of thes

122 Our prediction method complements experimental efforts, and

123 Understanding how RNA secondary structure prediction methods depend on the underlying nearest-neig

124 Recent improvements in the protein-structure prediction method developed in our laboratory, based on

125 er friendly website that gives access to the prediction method devised in this work.

126 The web server of the prediction method (DNCON) is available at http://iris.rn

127 Most effective structure prediction methods do not model the protein folding proc

128 xperiments demonstrated that our three-level prediction method effectively increased the recall of fu

129 Several protein function prediction methods employ structural features captured i

130 r the last decade in the accuracy of epitope prediction methods, especially for those that rely on th

131 vely when compared to other state of the art prediction methods, especially when sequence signal to r

132 The prediction method (EVfold_membrane) applies a maximum en

133 Most prediction methods exploit evolutionary sequence conserv

134 Many prediction methods face limitations in learning from the

135 Available tRNA prediction methods fail to accurately predict tRNASec, d

136 Hence, traditional sequence-based prediction methods focusing on a single residue (or a sh

137 this study, we aimed to develop a genotypic prediction method for antimicrobial susceptibilities.

138 pose a novel multi-classifier-based function prediction method for Drosophila melanogaster proteins,

139 We created SIFT Indel, a prediction method for frameshifting indels that has 84%

140 We recently developed a marker-guided prediction method for hybrid yield and showed a substant

141 In this work, we schemed out a new prediction method for low-similarity datasets using redu

142 , has emerged as an alternative to the motif prediction method for the identification of T cell epito

143 We propose a novel prediction method for the prediction of DNA-binding resi

144 y outperformed the existing state-of-the-art prediction method for the same purpose.

145 , we present BOCTOPUS2, an improved topology prediction method for transmembrane beta-barrels that ca

146 A new de novo protein structure prediction method for transmembrane proteins (FILM3) is

147 is therefore useful to develop computational prediction methods for DNA methylation.

148 m current homology-based secondary structure prediction methods for many proteins.

149 Reliable structure-prediction methods for membrane proteins are important b

150 standing can be used to design more powerful prediction methods for protein structural class.

151 We study these module prediction methods for simulated benchmark networks as w

152 cdotal due to the requirement of the contact prediction methods for the high volume of sequence homol

153 ree energy structure or suboptimal structure prediction methods for the purpose of comparison.

154 Protein structure prediction methods, for example, are capable of generati

155 show that grammar-based secondary structure prediction methods formulated as CLLMs consistently outp

156 Compared to previous PPI prediction methods, FpClass achieved better agreement wi

157 We have developed a novel gene prediction method FragGeneScan, which combines sequencin

158 omparison against the sequence-based contact prediction methods from CASP9, where our method presente

159 We show that our prediction method generalizes to pairs of neural oscilla

160 Traditional protein structure prediction methods generally use one or a few quality as

161 ggests a set of peptides for which different prediction methods give divergent predictions as to thei

162 We present a simple sequence-based SDP prediction method, GroupSim, and show that, surprisingly

163 To our knowledge, no prediction method has been demonstrated to be highly acc

164 sequence and structure based functional site prediction method has been implemented in a publicly ava

165 The results showed that the site-prediction methods have a low probability of identifying

166 In this study we show that the sequence prediction methods have accuracies nearly comparable to

167 proteins, many computational protein-protein prediction methods have been developed in the past.

168 In recent years many function prediction methods have been developed using various sou

169 Although many deleteriousness prediction methods have been developed, their prediction

170 Machine-learning prediction methods have been extremely productive in app

171 Several previous variant pathogenicity prediction methods have been proposed that quantify evol

172 biological noise, and current computational prediction methods have high false positive rates.

173 d contact-guided ab initio protein structure prediction methods have highlighted the importance of in

174 However, since the first studies contact prediction methods have improved.

175 Protein tertiary structure prediction methods have matured in recent years.

176 This paper presents a new indel functional prediction method HMMvar based on HMM profiles, which ca

177 This paper proposed a quantitative prediction method, HMMvar, to predict the effect of gene

178 Protein structure prediction methods, however, do not incorporate this pro

179 alysis has shown that MMSE has no value as a prediction method in determining minimal HE and in respe

180 This knowledge led to the development of a prediction method in which patches of surface residues w

181 thm outperformed other leading RNA structure prediction methods in both sensitivity and specificity w

182 and comprehensive assessment of the contact-prediction methods in different template conditions.

183 can greatly outperform the state-of-the-art prediction methods in identifying TISs.

184 By using transmembrane prediction methods in mouse and human orthologs, models

185 n ranks FunFHMMer as one of the top function prediction methods in predicting GO annotations for both

186 is consistently one of the best ranked fold prediction methods in the CAFASP and LiveBench competiti

187 sed to evaluate the performance of 13 domain prediction methods in the context of CAFASP-DP.

188 edictor remarkably outperformed the existing prediction methods in this field.

189 MetaPred2CS integrates six sequence-based prediction methods: in-silico two-hybrid, mirror-tree, g

190 ion) often outperforms a host of alternative prediction methods including random forests and penalize

191 Using six prediction methods, including least absolute shrinkage a

192 Prediction methods indicate that variants in seemingly h

193 hat while comparative analysis and in silico prediction methods indicate the presence of at least 28

194 e previous version of our tertiary structure prediction method, IntFOLD-TS.

195 The prediction method is based on solvent accessible surface

196 We find that the new CRM prediction method is superior to existing methods.

197 advantage of our approach over other operon prediction methods is that it does not require many expe

198 We test four popular function prediction methods (majority vote, weighted majority vot

199 A new generation of automated RNA structure prediction methods may help address these challenges but

200 nnotations, we used a sequence-based de novo prediction method, MetalDetector, to identify Cys and Hi

201 tation, we propose and develop a novel miRNA prediction method, miRank, based on our new random walks

202 as the basis for a quantitative miRNA target prediction method, miRNA targets by weighting immunoprec

203 tworks, we propose a novel essential protein prediction method, named SON, in this study.

204 We describe a target prediction method (NBmiRTar) that does not require seque

205 which makes the development of computational prediction methods of substantial interest.

206 We assessed our prediction method on an independent set of RNA-seq data

207 he accuracies of the three type III effector prediction methods on a small set of proteins not known

208 form a comprehensive assessment of 18 driver prediction methods on more than 3,400 tumor samples from

209 gorithm over current top-performing function prediction methods on the yeast and mouse proteomes acro

210 spite significant development in active-site prediction methods, one of the remaining issues is ranke

211 g loops hitherto uncharacterised by topology prediction methods or experimental approaches and 128 fa

212 g. obtained as a result of protein structure prediction methods or small molecule docking.

213 ssifiers and show that our cross-sample TFBS prediction method outperforms several previously describ

214 iled analysis of two sequence-based function prediction methods, PFP and ESG, which were developed in

215 h the development of a new protein interface prediction method, PredUs, that identifies what residues

216 jects as well as ab initio protein structure prediction methods provide structures of proteins with n

217 lief Network to combine the results of other prediction methods, providing a more accurate consensus

218 information predicted by two protein contact prediction methods PSICOV and DNcon to generate a new sc

219 Here, we present a new prediction method, pTARGET that can predict proteins tar

220 We surveyed nine poly(A) site prediction methods published between 1999 and 2011.

221 ths of most widely used BLAST-based function prediction methods, rarely used in function prediction b

222 The accuracy of a sequence-based antigenic prediction method relies on the choice of amino acids su

223 Interface prediction methods rely on a wide range of sequence, str

224 Traditional RNA function prediction methods rely on sequence or alignment informa

225 protein pairs (positive PPIs), computational prediction methods rely upon subsets of negative PPIs fo

226 re presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regul

227 Most of the current HGT prediction methods require pre-existing annotation, whic

228 ere, we report a new RNA secondary structure prediction method, restrained MaxExpect (RME), which can

229 The most effective prognostic prediction methods should use all available data, as thi

230 Our prediction method shows an area under the Receiver Opera

231 on, it is indispensable to develop different prediction methods since combining different methods may

232 is information into current RNA 3D structure prediction methods, specifically 3dRNA.

233 es, the performances and efficiencies of the prediction methods still need to be improved.

234 The prediction method successfully identifies sequence regio

235 tly in models generated by protein structure prediction methods such as Rosetta.

236 In general, learning-based pI prediction methods (such as Cofactor, SVM and Branca) re

237 e current top-performing secondary structure prediction methods, such as PHDpsi, PROFsec, SSPro2, JNE

238 Traditional template-based protein structure prediction methods tend to focus on identifying the best

239 Furthermore, a prediction method that can utilize low-resolution models

240 Here, we introduced PROFcon, a novel contact prediction method that combines information from alignme

241 re laboratory testing, we assessed if a risk prediction method that did not require any laboratory te

242 We propose a similarity-based drug-target prediction method that enhances existing association dis

243 s and humans through a genome-wide ab initio prediction method that enriches for exons involved in si

244 iDTI-ESBoost is a novel prediction method that has for the first time exploited

245 ubject to negative selection, we developed a prediction method that measures paucity of non-synonymou

246 on prediction (PFP) is an automated function prediction method that predicts Gene Ontology (GO) annot

247 iDNAProt-ES is a novel prediction method that uses evolutionary and structural

248 e present iDNAProt-ES, a DNA-binding protein prediction method that utilizes both sequence based evol

249 ntegrated into PrePPI, a structure-based PPI prediction method that, so far, has been limited to inte

250 Therefore, novel function prediction methods that do not rely on sequence or fold

251 Thus, gene prediction methods that explicitly take into account ins

252 nome, as compared with four current function prediction methods that precisely predicted function for

253 ore, there is a pressing need to develop new prediction methods that use an updated set of 14-3-3-bin

254 ant to developers and users of gene function prediction methods that use gene co-expression to indica

255 the original 3dRNA as well as other existing prediction methods that used the direct coupling analysi

256 the Rosetta low-resolution protein structure prediction method, that seeks the lowest energy tertiary

257 he opportunity for building binding affinity prediction methods, the accurate characterization of TF-

258 le computationally scalable execution of our prediction methods; these include SOAP and XML-RPC web s

259 ovides a small sample to train parameters of prediction methods, thus leading to low confidence.

260 SPOCS implements a graph-based ortholog prediction method to generate a simple tab-delimited tab

261 st of our knowledge, the first non-SIM based prediction method to have been tested directly on new da

262 ion approach using the proposed binding site prediction method to predict CaM binding proteins in Ara

263 differences and subsequently determine which prediction method to use would require further specifica

264 e applied state-of-the-art protein structure prediction methods to all 27 distinct MSY-encoded protei

265 logy in conjunction with ab initio structure prediction methods to define plausible shapes of DbpA.

266 using an ensemble of two secondary structure prediction methods to guide fragment selection in combin

267 does not respond, and the use of simple risk prediction methods to individualise the amount and type

268 nome sequence was analyzed with several gene prediction methods to produce a comprehensive gene list

269 and/or extended with existing and novel TIS prediction methods, to support further research efforts

270 Most IDR prediction methods use sequence profile to improve accur

271 Most ligand-binding sites prediction methods use the protein structures from the P

272 lso review many previous treatments of these prediction methods, use the latest available annotations

273 A comparison with other domain prediction methods used in the CASP7 competition indicat

274 The PrISE family of interface prediction methods uses a representation of structural e

275 s, we examined the reliabilities of the site-prediction methods, using nucleotide sequence data for t

276 Using the evolutionary prediction method USPEX, we found stable reconstructions

277 The performance of driver prediction methods varied considerably, with concordance

278 The prediction method was benchmarked on artificially prepar

279 A structure-prediction method was employed in order to investigate p

280 The prediction method was tested on artificially prepared se

281 dification sites and state-of-the-art target prediction methods we re-estimate the snoRNA target RNA

282 To demonstrate the generality of the prediction method, we have also applied the method to RN

283 Using this morphology prediction method, we identified two promising molecular

284 en Markov model-based transmembrane topology prediction method, we now propose a comprehensive topolo

285 ular mechanics (QM/MM) and protein structure prediction methods, we have modeled both the structural

286 r mechanics techniques and protein structure prediction methods, we provide a detailed electronic str

287 l sensitivity and specificity of the genomic prediction method were 0.97 (95% confidence interval [95

288 Alternatively, other prediction methods were based on the observation that mi

289 Five dose prediction methods were compared: 2 methods using only c

290 f Inheritance for Nonsynonymous variants), a prediction method which utilizes a random forest algorit

291 ble also separates our approach from epitope prediction methods which treat MHC alleles as symbolic t

292 ns than one of the leading protein structure prediction methods, which relies on a tailored Monte Car

293 ogenic viruses, we combined a new miRNA gene prediction method with small-RNA cloning from several vi

294 for development of new multiple localization prediction methods with higher coverage and accuracy.

295 rocess regression with several commonly used prediction methods with simulations.

296 ructure determination accuracies of sequence prediction methods with the empirically determined value

297 incorporates ConFunc, our existing function prediction method, with other approaches for function pr

298 Computer-assisted live-prediction method would be an additional approach to fac

299 this paper, we present a secondary structure prediction method YASPIN that unlike the current state-o

300 parameters used in state-of-the-art membrane prediction methods, yet achieves very high segment accur